{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inductive representation learning using GraphSAGE on the Pubmed-Diabetes citation network \n",
    "\n",
    "This notebook demonstrates inductive representation learning and node classification using the GraphSAGE [1] algorithm applied to inferring the subject of papers in a citation network.\n",
    "\n",
    "To demonstrate inductive representation learning, we train a GraphSAGE model on a subgraph of the Pubmed-Diabetes citation network. Next, we use the trained model to predict the subject of nodes that were excluded from the subgraph used for model training. \n",
    "\n",
    "We remove 20 percent of the network nodes (including all the edges from these nodes to any other nodes in the network) and then train a GraphSAGE model using this network comprised of the remaining 80 percent of nodes. For training, we only use 5 percent of labeled data.\n",
    "\n",
    "After training the model, we use it to predict the labels, i.e., paper subjects, of the nodes originally held out after re-inserting them in the network. For prediction, we do not retrain the GraphSAGE model.\n",
    "\n",
    "**References**\n",
    "\n",
    "[1] Inductive Representation Learning on Large Graphs. W.L. Hamilton, R. Ying, and J. Leskovec arXiv:1706.02216 [cs.SI], 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import itertools\n",
    "import os\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "import stellargraph as sg\n",
    "from stellargraph import globalvar\n",
    "from stellargraph.mapper import GraphSAGENodeGenerator\n",
    "from stellargraph.layer import GraphSAGE\n",
    "\n",
    "from tensorflow.keras import layers, optimizers, losses, metrics, Model\n",
    "from sklearn import preprocessing, feature_extraction, model_selection\n",
    "from stellargraph import datasets\n",
    "from IPython.display import display, HTML\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_pubmed(data_dir):\n",
    "    edgelist = pd.read_csv(\n",
    "        os.path.join(data_dir, \"Pubmed-Diabetes.DIRECTED.cites.tab\"),\n",
    "        skiprows=2,\n",
    "        header=None,\n",
    "        delimiter=\"\\t\",\n",
    "    )\n",
    "    edgelist.drop(columns=[0, 2], inplace=True)\n",
    "    edgelist.columns = [\"source\", \"target\"]\n",
    "    # delete unneccessary prefix\n",
    "    edgelist[\"source\"] = edgelist[\"source\"].map(lambda x: x.lstrip(\"paper:\"))\n",
    "    edgelist[\"target\"] = edgelist[\"target\"].map(lambda x: x.lstrip(\"paper:\"))\n",
    "    edgelist[\"label\"] = \"cites\"  # set the edge type\n",
    "\n",
    "    # Load the graph from the edgelist\n",
    "    g_nx = nx.from_pandas_edgelist(edgelist, edge_attr=\"label\")\n",
    "\n",
    "    # Load the features and subject for each node in the graph\n",
    "    nodes_as_dict = []\n",
    "    with open(\n",
    "        os.path.join(os.path.expanduser(data_dir), \"Pubmed-Diabetes.NODE.paper.tab\")\n",
    "    ) as fp:\n",
    "        for line in itertools.islice(fp, 2, None):\n",
    "            line_res = line.split(\"\\t\")\n",
    "            pid = line_res[0]\n",
    "            feat_name = [\"pid\"] + [l.split(\"=\")[0] for l in line_res[1:]][\n",
    "                :-1\n",
    "            ]  # delete summary\n",
    "            feat_value = [l.split(\"=\")[1] for l in line_res[1:]][:-1]  # delete summary\n",
    "            feat_value = [pid] + [\n",
    "                float(x) for x in feat_value\n",
    "            ]  # change to numeric from str\n",
    "            row = dict(zip(feat_name, feat_value))\n",
    "            nodes_as_dict.append(row)\n",
    "\n",
    "    # Create a Pandas dataframe holding the node data\n",
    "    node_data = pd.DataFrame(nodes_as_dict)\n",
    "    node_data.fillna(0, inplace=True)\n",
    "    node_data[\"label\"] = node_data[\"label\"].astype(int)\n",
    "    node_data[\"label\"] = node_data[\"label\"].astype(str)\n",
    "\n",
    "    node_data.index = node_data[\"pid\"]\n",
    "    node_data.drop(columns=[\"pid\"], inplace=True)\n",
    "    node_data.head()\n",
    "\n",
    "    for nid in node_data.index:\n",
    "        g_nx.nodes[nid][globalvar.TYPE_ATTR_NAME] = \"paper\"  # specify node type\n",
    "\n",
    "    feature_names = list(node_data.columns)\n",
    "    feature_names.remove(\"label\")\n",
    "\n",
    "    return g_nx, node_data, feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_history(history):\n",
    "    metrics = sorted(history.history.keys())\n",
    "    metrics = metrics[: len(metrics) // 2]\n",
    "    for m in metrics:\n",
    "        # summarize history for metric m\n",
    "        plt.plot(history.history[m])\n",
    "        plt.plot(history.history[\"val_\" + m])\n",
    "        plt.title(m)\n",
    "        plt.ylabel(m)\n",
    "        plt.xlabel(\"epoch\")\n",
    "        plt.legend([\"train\", \"test\"], loc=\"best\")\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "The PubMed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = datasets.PubMedDiabetes()\n",
    "display(HTML(dataset.description))\n",
    "dataset.download()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we are going to load the full graph and the corresponding node features and feature names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "g_nx, node_data, feature_names = load_pubmed(dataset.data_directory)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We aim to train a graph-ML model that will predict the \"label\" attribute on the nodes. These labels are one of 3 categories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1', '2', '3'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(node_data[\"label\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to **remove 20 percent of the nodes from the graph**. Then, we are going to train a GraphSAGE model on the reduced graph with the remaining 80 percent of the nodes from the original graph. Later, we are going to re-introduce the removed nodes and try to predict their labels without re-training the GraphSAGE model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19717, 501)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_data_sampled = node_data.sample(frac=0.8, replace=False, random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**node_data_sampled** is a Pandas dataframe that holds the node features and node IDs (the dataframe index) of the subgraph we are going to use for training the GraphSAGE model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15774, 501)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node_data_sampled.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we are going to extract the subgraph corresponding to the sampled nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "g_nx_sampled = g_nx.subgraph(node_data_sampled.index).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original graph number of nodes 19717 and edges 44327\n",
      "Sampled graph number of nodes 15774 and edges 28845\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    \"Original graph number of nodes {} and edges {}\".format(\n",
    "        g_nx.number_of_nodes(), g_nx.number_of_edges()\n",
    "    )\n",
    ")\n",
    "print(\n",
    "    \"Sampled graph number of nodes {} and edges {}\".format(\n",
    "        g_nx_sampled.number_of_nodes(), g_nx_sampled.number_of_edges()\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note above that both the number of nodes and edges have been reduced after removing 20 percent of the nodes in the original graph."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For machine learning we want to take a subset of the nodes for training, and use the rest for testing. We'll use scikit-learn to do this.\n",
    "\n",
    "We are going to use 5 percent of the data for training and of the remaining nodes, we are going to use 20 percent as the validation set. The other 80 percent we can treat as a test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data, test_data = model_selection.train_test_split(\n",
    "    node_data_sampled,\n",
    "    train_size=0.05,\n",
    "    test_size=None,\n",
    "    stratify=node_data_sampled[\"label\"],\n",
    "    random_state=42,\n",
    ")\n",
    "val_data, test_data = model_selection.train_test_split(\n",
    "    test_data,\n",
    "    train_size=0.2,\n",
    "    test_size=None,\n",
    "    stratify=test_data[\"label\"],\n",
    "    random_state=100,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note using stratified sampling gives the following counts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'1': 164, '3': 310, '2': 314})"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "Counter(train_data[\"label\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training set has class imbalance that might need to be compensated, e.g., via using a weighted cross-entropy loss in model training, with class weights inversely proportional to class support. However, we will ignore the class imbalance in this example, for simplicity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting to numeric arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our categorical target, we will use one-hot vectors that will be fed into a soft-max Keras layer during training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_encoding = feature_extraction.DictVectorizer(sparse=False)\n",
    "\n",
    "train_targets = target_encoding.fit_transform(train_data[[\"label\"]].to_dict(\"records\"))\n",
    "val_targets = target_encoding.transform(val_data[[\"label\"]].to_dict(\"records\"))\n",
    "test_targets = target_encoding.transform(test_data[[\"label\"]].to_dict(\"records\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now do the same for the node attributes we want to use to predict the subject. These are the feature vectors that the Keras model will use as input.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_features_sampled = node_data_sampled[feature_names]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>w-rat</th>\n",
       "      <th>w-common</th>\n",
       "      <th>w-use</th>\n",
       "      <th>w-examin</th>\n",
       "      <th>w-pathogenesi</th>\n",
       "      <th>w-retinopathi</th>\n",
       "      <th>w-mous</th>\n",
       "      <th>w-studi</th>\n",
       "      <th>w-anim</th>\n",
       "      <th>w-model</th>\n",
       "      <th>...</th>\n",
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       "          w-rat  w-common     w-use  w-examin  w-pathogenesi  w-retinopathi  \\\n",
       "pid                                                                           \n",
       "16221917    0.0  0.000000  0.160162  0.000000       0.000000            0.0   \n",
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       "9925350     0.0  0.018169  0.007445  0.024533       0.000000            0.0   \n",
       "17448371    0.0  0.000000  0.007499  0.000000       0.000000            0.0   \n",
       "\n",
       "          w-mous   w-studi  w-anim   w-model  ...  w-kidney  w-urinari  \\\n",
       "pid                                           ...                        \n",
       "16221917     0.0  0.006130     0.0  0.000000  ...       0.0        0.0   \n",
       "18591404     0.0  0.004186     0.0  0.000000  ...       0.0        0.0   \n",
       "16424231     0.0  0.013375     0.0  0.047765  ...       0.0        0.0   \n",
       "9925350      0.0  0.007409     0.0  0.000000  ...       0.0        0.0   \n",
       "17448371     0.0  0.007463     0.0  0.000000  ...       0.0        0.0   \n",
       "\n",
       "          w-myocardi  w-meal  w-ica  w-locus  w-tcell  w-depress    w-bone  \\\n",
       "pid                                                                          \n",
       "16221917         0.0     0.0    0.0      0.0      0.0        0.0  0.000000   \n",
       "18591404         0.0     0.0    0.0      0.0      0.0        0.0  0.102129   \n",
       "16424231         0.0     0.0    0.0      0.0      0.0        0.0  0.000000   \n",
       "9925350          0.0     0.0    0.0      0.0      0.0        0.0  0.000000   \n",
       "17448371         0.0     0.0    0.0      0.0      0.0        0.0  0.000000   \n",
       "\n",
       "          w-mutat  \n",
       "pid                \n",
       "16221917      0.0  \n",
       "18591404      0.0  \n",
       "16424231      0.0  \n",
       "9925350       0.0  \n",
       "17448371      0.0  \n",
       "\n",
       "[5 rows x 500 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node_features_sampled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sanity check to make sure that the node labels are not included in the node features\n",
    "\"label\" in node_features_sampled"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the GraphSAGE model in Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now create a StellarGraph object from the NetworkX graph and the node features and targets. It is StellarGraph objects that we use in this library to perform machine learning tasks on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = sg.StellarGraph(g_nx_sampled, node_features=node_features_sampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NetworkXStellarGraph: Undirected multigraph\n",
      " Nodes: 15774, Edges: 28845\n",
      "\n",
      " Node types:\n",
      "  paper: [15774]\n",
      "    Edge types: paper-cites->paper\n",
      "\n",
      " Edge types:\n",
      "    paper-cites->paper: [28845]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(G.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To feed data from the graph to the Keras model we need a generator. The generators are specialized to the model and the learning task so we choose the `GraphSAGENodeGenerator` as we are predicting node attributes with a GraphSAGE model.\n",
    "\n",
    "We need two other parameters, the `batch_size` to use for training and the number of nodes to sample at each level of the model. Here we choose a two-layer model with 10 nodes sampled in the first layer, and 10 in the second."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 50\n",
    "num_samples = [10, 10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A `GraphSAGENodeGenerator` object is required to send the node features in sampled subgraphs to Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = GraphSAGENodeGenerator(G, batch_size, num_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `generator.flow()` method, we can create iterators over nodes that should be used to train, validate, or evaluate the model. For training we use only the training nodes returned from our splitter and the target values. The `shuffle=True` argument is given to the `flow` method to improve training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_gen = generator.flow(train_data.index, train_targets, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can specify our machine learning model, we need a few more parameters for this:\n",
    "\n",
    " * the `layer_sizes` is a list of hidden feature sizes of each layer in the model. In this example we use 32-dimensional hidden node features at each layer.\n",
    " * The `bias` and `dropout` are internal parameters of the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "graphsage_model = GraphSAGE(\n",
    "    layer_sizes=[32, 32], generator=generator, bias=True, dropout=0.5,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we create a model to predict the 3 categories using Keras softmax layers. Note that we need to use the `G.get_target_size` method to find the number of categories in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_inp, x_out = graphsage_model.build()\n",
    "prediction = layers.Dense(units=train_targets.shape[1], activation=\"softmax\")(x_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([None, 3])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create the actual Keras model with the graph inputs `x_inp` provided by the `graph_model` and outputs being the predictions from the softmax layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model(inputs=x_inp, outputs=prediction)\n",
    "model.compile(\n",
    "    optimizer=optimizers.Adam(lr=0.005),\n",
    "    loss=losses.categorical_crossentropy,\n",
    "    metrics=[\"acc\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model, keeping track of its loss and accuracy on the training set, and its generalisation performance on the validation set (we need to create another generator over the validation data for this)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_gen = generator.flow(val_data.index, val_targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "history = model.fit_generator(\n",
    "    train_gen, epochs=15, validation_data=val_gen, verbose=0, shuffle=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have trained the model we can evaluate on the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_gen = generator.flow(test_data.index, test_targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test Set Metrics:\n",
      "\tloss: 0.5556\n",
      "\tacc: 0.8117\n"
     ]
    }
   ],
   "source": [
    "test_metrics = model.evaluate_generator(test_gen)\n",
    "print(\"\\nTest Set Metrics:\")\n",
    "for name, val in zip(model.metrics_names, test_metrics):\n",
    "    print(\"\\t{}: {:0.4f}\".format(name, val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making predictions with the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to use the trained model to predict the nodes we put aside earlier. For this, we must create a new StellarGraph object and a new node generator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_features = node_data[feature_names]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19717, 500)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node_data[feature_names].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph **G** we are going to create next is a `StellarGraph` object representing the entire citation network, with the hold-out nodes restored."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "G = sg.StellarGraph(g_nx, node_features=node_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The new generator feeds data from this full graph into the model trained on the sampled partial graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = GraphSAGENodeGenerator(G, batch_size, num_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's get the predictions themselves for all nodes in the hold out set. We are going to use an iterator `generator.flow()` over the hold-out nodes for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_data_hold_out = pd.concat([node_data_sampled, node_data]).drop_duplicates(keep=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3941, 501)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node_data_hold_out.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "hold_out_targets = target_encoding.transform(\n",
    "    node_data_hold_out[[\"label\"]].to_dict(\"records\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "hold_out_nodes = node_data_hold_out.index\n",
    "hold_out_gen = generator.flow(hold_out_nodes, hold_out_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have a generator for our hold out data, we can use our trained model to make predictions for them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "hold_out_predictions = model.predict_generator(hold_out_gen)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These predictions will be the output of the softmax layer, so to get final categories we'll use the `inverse_transform` method of our target attribute specifcation to turn these values back to the original categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "hold_out_predictions = target_encoding.inverse_transform(hold_out_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3941"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(hold_out_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at a few:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2344352</th>\n",
       "      <td>label=1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2684155</th>\n",
       "      <td>label=3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10960717</th>\n",
       "      <td>label=1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15723700</th>\n",
       "      <td>label=2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16118269</th>\n",
       "      <td>label=1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11756346</th>\n",
       "      <td>label=3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11731221</th>\n",
       "      <td>label=3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9356032</th>\n",
       "      <td>label=3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16219016</th>\n",
       "      <td>label=3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11980626</th>\n",
       "      <td>label=3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Predicted True\n",
       "pid                    \n",
       "2344352    label=1    1\n",
       "2684155    label=3    1\n",
       "10960717   label=1    1\n",
       "15723700   label=2    3\n",
       "16118269   label=1    1\n",
       "11756346   label=3    3\n",
       "11731221   label=3    3\n",
       "9356032    label=3    3\n",
       "16219016   label=3    3\n",
       "11980626   label=3    3"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame(hold_out_predictions, index=hold_out_nodes).idxmax(axis=1)\n",
    "df = pd.DataFrame({\"Predicted\": results, \"True\": node_data_hold_out[\"label\"]})\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Hold Out Set Metrics:\n",
      "\tloss: 0.5474\n",
      "\tacc: 0.8188\n"
     ]
    }
   ],
   "source": [
    "hold_out_metrics = model.evaluate_generator(hold_out_gen)\n",
    "print(\"\\nHold Out Set Metrics:\")\n",
    "for name, val in zip(model.metrics_names, hold_out_metrics):\n",
    "    print(\"\\t{}: {:0.4f}\".format(name, val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that inductive performance of the model on the hold out nodes (not present in the graph during training) is on par with the performance on the test set of nodes that were present in the graph during training, but whose labels were concealed. This demonstrates good inductive performance of GraphSAGE model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Node embeddings for hold out nodes\n",
    "\n",
    "We are going to extract node embeddings as activations of the output of graphsage layer stack, and visualise them, coloring nodes by their subject label.\n",
    "\n",
    "The GraphSAGE embeddings are the output of the GraphSAGE layers, namely the `x_out` variable. Let's create a new model with the same inputs as we used previously `x_inp` but now the output is the embeddings rather than the predicted class. Additionally note that the weights trained previously are kept in the new model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_model = Model(inputs=x_inp, outputs=x_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3941, 32)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emb = embedding_model.predict_generator(hold_out_gen)\n",
    "emb.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Project the embeddings to 2d using either TSNE or PCA transform, and visualise, coloring nodes by their subject label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = emb\n",
    "y = np.argmax(\n",
    "    target_encoding.transform(node_data_hold_out[[\"label\"]].to_dict(\"records\")), axis=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "if X.shape[1] > 2:\n",
    "    transform = TSNE  # PCA\n",
    "\n",
    "    trans = transform(n_components=2)\n",
    "    emb_transformed = pd.DataFrame(trans.fit_transform(X), index=node_data_hold_out.index)\n",
    "    emb_transformed[\"label\"] = y\n",
    "else:\n",
    "    emb_transformed = pd.DataFrame(X, index=node_data_hold_out.index)\n",
    "    emb_transformed = emb_transformed.rename(columns={\"0\": 0, \"1\": 1})\n",
    "    emb_transformed[\"label\"] = y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha = 0.7\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "ax.scatter(\n",
    "    emb_transformed[0],\n",
    "    emb_transformed[1],\n",
    "    c=emb_transformed[\"label\"].astype(\"category\"),\n",
    "    cmap=\"jet\",\n",
    "    alpha=alpha,\n",
    ")\n",
    "ax.set(aspect=\"equal\", xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n",
    "plt.title(\n",
    "    \"{} visualization of GraphSAGE embeddings of hold out nodes for pubmed dataset\".format(\n",
    "        transform.__name__\n",
    "    )\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook demonstrated inductive representation learning and node classification using the GraphSAGE algorithm.\n",
    "\n",
    "More specifically, the notebook demonstrated how to use the `stellargraph` library to train a GraphSAGE model using one network and then use this trained model to predict node attributes for a different (but similar) network.\n",
    "\n",
    "Classsification accuracy on the latter network was on par with classification accuracy on a set of training network test nodes with hidden labels."
   ]
  }
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